Linear Decreasing Inertia Weight Research Articles

Photovoltaic array prediction on short-term output power method in Centralized power generation system

The photovoltaic array directly determines the output power system of the entire photovoltaic power generation system. In order to more accurately predict the output power of the photovoltaic power generation system and reduce the impact of photovoltaic power generation on the power system, this study proposes a prediction model based on an improved firefly algorithm optimized support vector machine. The model introduces the linear decreasing inertia weight and the adaptive variable step-size in the original firefly algorithm that effectively improves the convergence speed and optimization ability of the algorithm. The multiple meteorological factors influencing the photovoltaic power generation were studied. Calculate the correlation coefficient of each meteorological influencing factor between the forecasted date and historical date to determine the training sample. The training samples were used to train the prediction model. The photovoltaic array output power in the sunny, cloudy and rainy days was predicted for the three weather styles using the trained prediction model. The results were compared the prediction results on the standard firefly algorithm-based optimizing support vector machine and particle swarm algorithm-based optimizing support vector machines. The proposed method showed that the mean absolute percentage error of the three-weather style prediction result is reduced by 1.66 and 3.30% and the mean square error is reduced by 0.21 and 0.27 compared to other methods. This method is verified to predict the PV array output power more accurately.

Effects of Random Values for Particle Swarm Optimization Algorithm

Particle swarm optimization (PSO) algorithm is generally improved by adaptively adjusting the inertia weight or combining with other evolution algorithms. However, in most modified PSO algorithms, the random values are always generated by uniform distribution in the range of [0, 1]. In this study, the random values, which are generated by uniform distribution in the ranges of [0, 1] and [−1, 1], and Gauss distribution with mean 0 and variance 1 ( U [ 0 , 1 ] , U [ − 1 , 1 ] and G ( 0 , 1 ) ), are respectively used in the standard PSO and linear decreasing inertia weight (LDIW) PSO algorithms. For comparison, the deterministic PSO algorithm, in which the random values are set as 0.5, is also investigated in this study. Some benchmark functions and the pressure vessel design problem are selected to test these algorithms with different types of random values in three space dimensions (10, 30, and 100). The experimental results show that the standard PSO and LDIW-PSO algorithms with random values generated by U [ − 1 , 1 ] or G ( 0 , 1 ) are more likely to avoid falling into local optima and quickly obtain the global optima. This is because the large-scale random values can expand the range of particle velocity to make the particle more likely to escape from local optima and obtain the global optima. Although the random values generated by U [ − 1 , 1 ] or G ( 0 , 1 ) are beneficial to improve the global searching ability, the local searching ability for a low dimensional practical optimization problem may be decreased due to the finite particles.

Intelligent Modeling Method for a Combined Radiation-Convection Grain Dryer: A Support Vector Regression Algorithm Based on an Improved Particle Swarm Optimization Algorithm

This paper aims to investigate an intelligent model for a combined infrared radiation-convection (IRC) dryer by using a support vector regression (SVR) algorithm based on an improved particle swarm optimization algorithm (IPSO). The IPSO algorithm was designed to optimize the parameters of the SVR algorithm, which has improved the optimization ability of the IPSO [linear decreasing inertia weight (LDIW)] algorithm and the standard PSO algorithm by introducing a relative fitness deviation (FD) to the LDIW equation and by combining a concept of mutation. Based on the data collected from a practical drying experiment, the best IPSO-SVR (LDIW-FD) model for the IRC dryer was successfully constructed, and the prediction performance comparisons of different modeling methods were also made. The resulting IPSO-SVR (LDIW-FD) model has achieved a remarkable predictive accuracy compared with the other models, demonstrating the effectiveness of the proposed model. In addition, a model of concurrent-counter flow drying was also successfully established by using the same method, depicting the proposed method can be readily used to precisely predict different drying processes. This modeling method can give relatively good predictive output information of the nonlinear system, and it may provide an accurate model for the prediction control of grain drying.

Research on Chaotic Firefly Algorithm and the Application in Optimal Reactive Power Dispatch

Firefly algorithm (FA) is a newly proposed swarm intelligence optimization algorithm. The original version of FA usually traps into local optima like many other general swarm intelligence optimization algorithm. In order to overcome this drawback, the chaotic firefly algorithm(CFA) is proposed. The methods of chaos initialization, chaos population regeneration and linear decreasing inertia weight have been introduced into the original version of FA so as to increase its global search mobility for robust global optimization. The CFA is calculated in Matlab and is examined on six benchmark functions. In order to evaluate the engineering application of the algorithm, the reactive power optimization problem in IEEE 30 bus system is solved by CFA. The outcomes show that the CFA has better performance compared to the original version of FA and PSO

A PSO-GRNN Model for Railway Freight Volume Prediction: Empirical Study from China

Purpose: The purpose of this paper is to propose a mathematical model for the prediction of railway freight volume, and therefore provide railway freight resource allocation with an accurate direction. With an accurate railway freight volume prediction, railway freight enterprises can integrate the limited resources and organize transport more reasonably. Design/methodology/approach: In this paper, a PSO-GRNN model is proposed to predict the railway freight volume. In this model, GRNN is applied to carry out the nonlinear regression analysis and output the prediction value, PSO algorithm is applied to optimize the GRNN model by searching the best smoothing parameter. In order to improve the performance of PSO algorithm, time linear decreasing inertia weight algorithm and time varying acceleration coefficient algorithm are applied in the paper. Originality/value: A railway freight volume prediction index system containing seventeen indexes from five aspects is established in this paper. And PSO-GRNN model constructed in this paper are applied to predict the railway freight volume from 2007 to 2011. Finally, an empirical study is given to verify the feasibility and accuracy of the PSO-GRNN model by comparing with RBFNN model and BPNN model. The result shows that PSO-GRNN model has a good performance in reducing the prediction error, and can be applied in actual production easily.

Novel Particle Swarm Optimization and Its Application in Calibrating the Underwater Transponder Coordinates

A novel improved particle swarm algorithm named competition particle swarm optimization (CPSO) is proposed to calibrate the Underwater Transponder coordinates. To improve the performance of the algorithm, TVAC algorithm is introduced into CPSO to present anextension competition particle swarm optimization(ECPSO). The proposed method is tested with a set of 10 standard optimization benchmark problems and the results are compared with those obtained through existing PSO algorithms,basic particle swarm optimization(BPSO),linear decreasing inertia weight particle swarm optimization(LWPSO),exponential inertia weight particle swarm optimization(EPSO), andtime-varying acceleration coefficient(TVAC). The results demonstrate that CPSO and ECPSO manifest faster searching speed, accuracy, and stability. The searching performance for multimodulus function of ECPSO is superior to CPSO. At last, calibration of the underwater transponder coordinates is present using particle swarm algorithm, and novel improved particle swarm algorithm shows better performance than other algorithms.

An Investigation into the Performance of Particle Swarm Optimization with Various Chaotic Maps

This paper experimentally investigates the effect of nine chaotic maps on the performance of two Particle Swarm Optimization (PSO) variants, namely, Random Inertia Weight PSO (RIW-PSO) and Linear Decreasing Inertia Weight PSO (LDIW-PSO) algorithms. The applications of logistic chaotic map by researchers to these variants have led to Chaotic Random Inertia Weight PSO (CRIW-PSO) and Chaotic Linear Decreasing Inertia Weight PSO (CDIW-PSO) with improved optimizing capability due to better global search mobility. However, there are many other chaotic maps in literature which could perhaps enhance the performances of RIW-PSO and LDIW-PSO more than logistic map. Some benchmark mathematical problems well-studied in literature were used to verify the performances of RIW-PSO and LDIW-PSO variants using the nine chaotic maps in comparison with logistic chaotic map. Results show that the performances of these two variants were improved more by many of the chaotic maps than by logistic map in many of the test problems. The best performance, in terms of function evaluations, was obtained by the two variants using Intermittency chaotic map. Results in this paper provide a platform for informative decision making when selecting chaotic maps to be used in the inertia weight formula of LDIW-PSO and RIW-PSO.

Analog Circuit Fault Diagnosis Based on Particle Swarm Neural Network Hybrid Algorithm

Base on improved particle swarm algorithm, this paper proposes a linear decreasing inertia weight particle swarm algorithm and error back propagation algorithm based on hybrid algorithm combining. The linear decreasing inertia weight particle swarm algorithm and momentum-adaptive learning rate BP algorithm interchangeably adjust the network weights, so that the two algorithms are complementary. It gives full play to the PSO's global optimization ability and the BP algorithm local search advantage, to overcome the slow convergence speed and easily falling into local weight problems. Simulation results show that this diagnostic method can be used for tolerance analog circuit fault diagnosis, with a high convergence rate and diagnostic accuracy.

On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization

Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the algorithm approaches its terminal point. Researchers have tried to address this shortcoming by modifying LDIW-PSO or proposing new PSO variants. Some of these variants have been claimed to outperform LDIW-PSO. The major goal of this paper is to experimentally establish the fact that LDIW-PSO is very much efficient if its parameters are properly set. First, an experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems. Second, using the experimentally obtained values, five well-known benchmark optimization problems were used to show the outstanding performance of LDIW-PSO over some of its competitors which have in the past claimed superiority over it. Two other recent PSO variants with different inertia weight strategies were also compared with LDIW-PSO with the latter outperforming both in the simulation experiments conducted.

Global minimization of multi-funnel functions using particle swarm optimization

In this paper, we consider the problem of finding the global minimum of multi-funnel-shaped functions with many local minima, which is a well-known and interesting problem in computational biology. First, the particle swarm optimization algorithms are briefly reviewed. Then, we have applied a variant of it with linear decreasing inertia weight to solve the underlying global optimization problem. Our computational experiments on several known test problems show the efficiency of the particle swarm optimization algorithm in comparison with global convex quadratic underestimator algorithms that are widely used in the literature.

Research and Design of Adaptive Noise Cancellation Based on Particle Swarm Optimization Algorithm

Particle swarm optimization (PSO) is a kind of evolutionary computation technology which simulates the behavior of biological species. The essence of adaptive noise cancellation (ANC) is adjust the weight value of filter based on the input signals, the LMS algorithm is commonly used in this system, However, the convergence behavior and maladjustment of the LMS algorithm is seriously affected by the step-size μ, and the optimum value of μ cannot be determined easily, In this paper, Particle Swarm Optimization with linear decreasing inertia weight is proposed to solve the filter problem instead of LMS, taking the FIR filter of ANC as example, the simulation shows that ANC based on the PSO algorithm is better than classic ANC based on the LMS algorithm, and it gives the satisfactory results.

Optimization of Maximum Entropy Model Oriented to Bayes Prior Distribution Based on PSO

How to acquire prior distribution is a key to Bayes method. Firstly, a nonlinear constrained optimal model of probability density function based on the principle of maximum entropy is set up. By using Lagrange multiplier this constrained optimal problem is transformed to a non-constrained optimal one, which is solved by standard particle swarm optimization (PSO) algorithm. Secondly, a new improved particle swarm optimization (IPSO) algorithm is proposed because standard PSO is slow on convergence and easy to be trapped in local optimum. IPSO introduces the hybrid method from genetic algorithm (GA) so that the overall searching ability is enhanced; then linear decreasing inertia weight is used to optimize particles. The simulation examples show that IPSO is simple and effective and it can rapidly converge with high quality solutions.